TY - GEN
T1 - Bumil Bahagia Smart Home System
T2 - 7th International Conference of Computer and Informatics Engineering, IC2IE 2024
AU - Fatichin, Mochammad Rizqul
AU - Aulia Vinabrti, Retno
AU - Muklason, Ahmad
AU - Riksakomara, Edwin
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Postpartum Depression is a mental health disorder in postpartum mothers. Overcoming the anxiety at an early stage are important to prevent and reduce symptoms and their impact on maternal health and child development. Music therapy is one of the treatments that can give soothness and is easy to apply. In providing therapy, the selection of music that can soothe and match with mother's preference is important. This study aims to develop a soothing music recommendations system for mothers at risk of PPD. The recommendations are made based on the similarity of music, considering the music genre classification. The music genre classification was built by utilizing the musical features that generated from audio signal processing and using the Support Vector Machine (SVM) algorithm as a classifier. Five music recommendations were generated based on the similarity between music features which are good for classifying genres. The results of this study show that the genre classification, that is part of the recommendation system, has an accuracy value of 80% with a precision of 82% and a recall of 80%. Removing some redundant and irrelevant features can improve the quality of music genre classification.
AB - Postpartum Depression is a mental health disorder in postpartum mothers. Overcoming the anxiety at an early stage are important to prevent and reduce symptoms and their impact on maternal health and child development. Music therapy is one of the treatments that can give soothness and is easy to apply. In providing therapy, the selection of music that can soothe and match with mother's preference is important. This study aims to develop a soothing music recommendations system for mothers at risk of PPD. The recommendations are made based on the similarity of music, considering the music genre classification. The music genre classification was built by utilizing the musical features that generated from audio signal processing and using the Support Vector Machine (SVM) algorithm as a classifier. Five music recommendations were generated based on the similarity between music features which are good for classifying genres. The results of this study show that the genre classification, that is part of the recommendation system, has an accuracy value of 80% with a precision of 82% and a recall of 80%. Removing some redundant and irrelevant features can improve the quality of music genre classification.
KW - audio signal processing
KW - music recommendation system
KW - postpartum depression
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85212679573&partnerID=8YFLogxK
U2 - 10.1109/IC2IE63342.2024.10748191
DO - 10.1109/IC2IE63342.2024.10748191
M3 - Conference contribution
AN - SCOPUS:85212679573
T3 - Proceedings 7th IC2IE 2024 - 2024 International Conference of Computer and Informatics Engineering: Generative AI in Democratizing Access to Knowledge and Skills
BT - Proceedings 7th IC2IE 2024 - 2024 International Conference of Computer and Informatics Engineering
A2 - Muharram, Asep Taufik
A2 - Rosalina, Mira
A2 - Kurniawati, Dewi
A2 - Yulianti, Susana Dwi
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 September 2024 through 13 September 2024
ER -
